NONE 20260505061616104 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 1109-2734 2224-266X Chaimae Ouchicha Department of Management Assistance Techniques, Hassan 1st University (UH1), ENCG, FAMISDS Lab, Settat, MOROCCO December 2019 marked the emergence of a novel coronavirus in China that rapidly spread worldwide. Early detection of COVID-19 is crucial to limit transmission. The standard diagnostic method, reverse transcription-polymerase chain reaction (RT-PCR), is accurate but costly and time-consuming. Recent studies indicate that deep learning models can reliably detect COVID-19 from chest X-ray (CXR) images, offering a faster and more cost-effective alternative. In this paper, we propose COVID-MRSNet, a deep Convolutional Neural Network-based ResNet architecture for classifying COVID-19, viral pneumonia, and normal cases using CXR images. The model was trained and evaluated on a public dataset of 2,905 images, including 219 COVID- 19, 1,345 viral pneumonia, and 1,341 normal cases. After 50 epochs, COVID-MRSNet achieved an average accuracy of 98.34%, recall of 98.67%, precision of 98.55%, and F1-score of 98.61%. The results demonstrate superior multi-class classification performance compared to existing CNN-based methods. 05 05 2026 05 05 2026 66 7 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23201.2026.25.7 https://wseas.com/journals/cas/2026/a145101-005(2026).pdf Tao, A. and Zhenlu, Y. and Hongyan, H. and Chenao, Z. and Chong, C. and Wenzhi, L. and Qian, T. and Ziyong, S. and Liming, X. (2020) Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases, Radiology, Vol. 296, No. 2, p. 200642. https://doi.org/10.1148/radiol.2020200642 Kanne, J. P., Little, B. P., Chung, J. H., Elicker, B. M., and Ketai, L. H. 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